Many existing approaches for tracking an object operate by matching characteristics of the object over multiple frames of a video sequence. Some tracking approaches use a position of an object or a size of an object. Performing object tracking using the position and size of an object can lead to incorrect tracking when multiple objects appear in a similar location with a similar size, since an object may be associated with the wrong track. Using an appearance of an object in addition to the position and size of the object can assist in preventing incorrect associations between two similarly located objects, where those two objects have difference appearances. An appearance of an object may relate, for example, to color or textures of the object.
One approach for tracking an object based on the appearance of the object uses a summary of the appearance of the object, known as a signature. In one approach, the signature is a histogram of color components associated with the object. A signature of an object detected in a current frame can be compared with a signature of an object from a previous frame. If there is a high similarity in the signatures and also in the spatial characteristics of the object, the object in the current frame can be associated with the object in the previous frame. Thus, tracking of the object over multiple frames is achieved. The spatial characteristics of the object may include, for example, the location and size of the object. However, this approach does not allow for tracking of objects having a varying appearance over time.
One approach for tracking an object having a varying appearance is a Clustering Method. Object signatures from a training data set are accumulated and clustering is performed on the training data to determine an exemplary signature for each cluster. When tracking an object, new signatures are added to the clusters, and the exemplary signatures of the clusters are determined again. One disadvantage with this method is that it requires many signatures to be stored in memory, and the method requires significant computational resources for the re-estimation of clusters. Another disadvantage with this method is that a clustering approach may fail when the collection of signatures do not form distinct clusters. Further, training data may not always be available in a practical application. Because of the nature of clustering algorithms, a completely new appearance of an object may not be represented in the exemplary signatures until sufficient occurrences of the new appearance have been observed. Hence, the Clustering Method results in a long initialization period for each new appearance of an object.
Another approach for tracking an object having a varying appearance over time is an Eigenbasis Method. The Eigenbasis Method constructs an eigenbasis from observed signatures. The eigenbasis reflects the principal components of the signature. By weighting each of the components of the eigenbasis and summing the weighted components, a signature of an object can be reconstructed. As further signatures of an object are acquired over time, the eigenbasis is updated, at the cost of computational efficiency. Further, as more signatures are acquired, less importance is placed on older signatures. Thus, the eigenbasis models the recent appearance of an object. As a result, the reoccurrence of an old appearance may not be recognized, since the eigenbasis represents more recent appearances. For example, if a person turns around from an initial orientation, the appearance of the person changes to a new appearance. When the same person turns back to the initial orientation, the eigenbasis approach treats this current orientation of the person as a new appearance, while really the current appearance of the person is a reoccurrence of an old appearance. Failing to treat the current appearance as a reoccurrence of the old appearance results in the creation of new tracks, where there really is one continuous track. Further, the eigenbasis approach is computationally expensive, because the eigenbasis approach must re-determine the eigenbasis to incorporate each new appearance of an object.
A further approach for tracking an object having a varying appearance over time is a Component-Based Method. The Component-Based Method models the signature of an object as two components: a stable component and a transient component. The stable component models a slowly changing part of the signature and is updated in small increments. The transient component models rapid, and possibly temporary, changes in the signature, and is frequently updated in larger amounts. The actual signature of the object is represented by a weighted sum of these two components. The weights applied to the components reflect the confidence in the stability of the signature. This enables the Component-Based Method to model the average signature whilst allowing for significant temporary variations. The Component-Based Method cannot distinguish between a temporary variation and a completely new appearance. As a consequence, when there are multiple objects, objects may be assigned to the wrong track. Further, due to the nature of the Component-Based Method, periodic changes in a signature can only be stored in the transient component of the signature. Frequent and significant updates to the transient component indicate that there is low confidence in the stability of the object signature. Thus, the appearances of objects that undergo periodic motion whilst in a stable configuration, such as a person walking, cannot be modelled with high confidence. To recover from low confidence in an appearance of an object, a re-initialization period is required during which the appearance of the object remains stable.
Thus, a need exists for an improved method of tracking an object having a varying appearance over time.